Method for evaluation of hydrocarbon content of shale

ABSTRACT

The invention relates to the evaluation of hydrocarbon gas or liquid deposits, or condensate, in a shale formation. From relatively few log inputs, together with assumed or estimated or known values for density or porosity of kerogen, a single mathematical process involving the solution of a number of simultaneous equations, provides a value for both kerogen volume and total porosity. Additional checks and balances may be used to provide corrections to the result, for example based on pyrite volume or water saturation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional application which claims the benefit of and priority to U.S. provisional application Ser. No. 61/495,186 dated Jun. 9, 2011, entitled “Method for evaluation of hydrocarbon content of shale,” which is hereby incorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

FIELD OF THE INVENTION

This invention relates to the evaluation of the hydrocarbon content, for example the hydrocarbon gas and/or liquid content, of a subterranean shale deposit.

BACKGROUND OF THE INVENTION

Shale is an increasingly important source of hydrocarbon resources. Such unconventional resources, however, present challenges not only in their extraction but also in the analysis of a deposit to determine its hydrocarbon content. Clearly, analysis of the potential of a shale deposit prior to committing to the substantial cost of extracting the hydrocarbon is essential. Analysis is also essential to inform and guide the location, development and completion of wells in the deposit.

The evaluation of shale resources, especially shale gas resources, is challenging because of a variety of factors including low values of porosity and permeability, and complicated and variable mineralogy.

Shale gas formations can also contain oil and valuable condensate deposits. The formations are generally characterized by low to moderate clay volumes and low to high quartz or calcite content, where decreasing quartz volume is generally offset with increasing calcite volume. These formations also often contain small but significant amounts of heavy minerals, usually dominated by pyrite (and marcasite). The formations may contain up to 12 to 15% by volume of kerogen (organic matter, transformed by heat and pressure) which is a source of methane gas and liquid hydrocarbons present in pores in the rock. There is normally also adsorbed gas present in association with the kerogen; the kerogen itself is porous and can contain gas. Finally, the formations normally contain trace amounts of uranium and other radioactive elements which can render the Gamma-Ray log essentially useless for quantitative interpretation of clay content.

A great deal of effort has been expended in attempting to develop methods for evaluation of shale gas formations. Much of this effort has involved efforts to use specialized logging measurements such as spectral elemental analysis to solve for all of the significant elements present in the formation. However, the volume of kerogen, which is one of the most important parameters, cannot be determined with the spectral tools due to the presence of carbon in various minerals as well as in the kerogen. Independent methods are normally used to determine the volume of organic material.

Kerogen from logs is typically determined using the method outlined in the paper: Passey, Q., et al., A practical model for organic richness from porosity and resistivity logs, AAPG Bulletin, 74, No. 12, p. 1777-1794. The Passey method yields kerogen content, but nothing more. Other formation properties such as porosity and water saturation must be determined independently. The Passey method was originally derived for use in evaluation of the total organic content of hydrocarbon source rocks. The method requires knowledge of the maturity of the organic material, and it is less accurate for sediments that are over-mature, such as shale gas formations. See also: Schmoker, James W. and Hester, Timothy C., 1983, Organic carbon in Bakken Formation, United States portion of Williston Basin: AAPG Bulletin, v. 67, no. 12, p. 2165-2174.

This sort of approach involves use of geochemical or spectral logs (which measure elemental composition of the formation), which are then combined with conventional logs, such as sonic slowness (DT), gamma ray (GR), bulk density (RHOB), and resistivity (R_(t)) to determine the mineralogical composition of the formation along with porosity and water saturation. These methods rely on empirical correlations between kerogen volume and formation bulk density (or other logs) to compute kerogen volume. This is then used as an input curve in the multi-mineral solution that also yields porosity. These methods require multiple input curves that increase the cost of data acquisition and complexity of data analysis.

Still other methods utilize apparent matrix methods, where the mineralogically complex shale gas formations are represented by apparent values of matrix response for each of the input logs. The interpretation depends strongly on the judgment of the interpreter in selecting the values of the apparent matrix property for each log.

Generally, in these prior methods, the volume of kerogen is first derived using any one of a variety of techniques. The value is then used together with log data in conventional methodologies to derive a value for the total porosity of the formation. These methods do not distinguish between porosity of the mineral matrix and porosity contained within the kerogen. This is shown diagrammatically in FIG. 2 a.

The overall porosity Φ_(t) (sometimes referred to as “Phit”) gives a good indication of hydrocarbon content, particularly if combined with a figure for water saturation (that is to say, how much of the total porosity of the shale, which would otherwise be occupied by hydrocarbon, is in fact occupied by water).

Other approaches use brute-force empirical methods to calibrate models directly from core data. These methods require a full suite of conventional logs and abundant core data to use in establishing the correlations between logs and core data. These methods will work only if there is no change in the log to core correlation that might be present due to changes in geology.

For example, where there is plentiful core data, at least in a small number of key wells, it's possible to use neural networks or the so-called clustering technique. Well log and core measurements acquired in a handful of wells are used to derive an empirical correlation between the desired and measurable parameters. Measurable parameters may include e.g. gamma ray, bulk density, neutron porosity, photoelectric factor and deep sensing conductivity (1/R_(t)). Desired parameters to be predicted may include volume of kerogen, total porosity, grain density, total water saturation and gas-filled porosity. The principal disadvantage of this technique is the reliance on abundant core data in a given formation.

To summarize, the prior models for evaluating shale gas do not intrinsically include kerogen. Instead, kerogen volume is predicted as described above, e.g. using empirical relationships between core kerogen content and other logs (e.g. gamma ray or bulk density). Once kerogen volume is estimated, then overall porosity and water content are calculated using established techniques.

There is a need for a method of evaluating hydrocarbon content, especially hydrocarbon gas content, in a shale deposit, which is simple, fast and relatively accurate and which takes into account kerogen.

BRIEF SUMMARY OF THE DISCLOSURE

The inventors have realized that kerogen volume, together with total porosity, in a shale deposit may in fact be estimated with a reasonable degree of accuracy from a small number of logs without resorting to expensive, complicated and potentially unreliable elemental analysis, and which does not rely on core data, but may be verified by subsequent core sampling if necessary.

In the new technique, kerogen with associated porosity may be an intrinsic part of the model—see FIG. 3. Either or both of the density of solid kerogen and the porosity of kerogen may be known with reasonable accuracy and can be used as inputs. The measured bulk density (RHOB log), together with one or more measurements indicative of porosity and water content (e.g. DT log, resistivity log) may then be mathematically combined with the kerogen density and/or kerogen porosity to give an estimate for solid kerogen volume and total porosity and water content. See FIG. 2 b.

In one embodiment, a method for evaluating the volume of hydrocarbon gas or liquid in a shale deposit comprises combining known kerogen density and/or kerogen porosity values with log data in a mathematical analysis to derive directly values for kerogen volume, total porosity and water content. Kerogen volume can be either solid kerogen volume or porous kerogen volume; since the kerogen porosity is known, either value can easily be derived from the other.

In this method, the mathematical analysis may comprise the solution of simultaneous equations incorporating said known kerogen density and/or kerogen porosity values and log data. The log data may include: (i) log measurements of bulk density (RHOB) and (ii) log measurements indicative of porosity. The log measurements indicative of porosity may comprise either or both of neutron log measurements and slowness measurements, and may also include resistivity, which may provide an indication of water saturation.

The mathematical analysis may incorporate at least one known, assumed or estimated parameter from the group comprising: solid matrix grain density, matrix slowness and slowness of solid kerogen, or it may incorporate all of these.

Another option is to adjust the derived value for kerogen volume to be consistent with a value for pyrite and/or marcasite volume (which may be obtained using X ray diffraction) based on an empirically derived relationship between kerogen volume and pyrite and/or marcasite volume; this can be done using an iterative process.

If the derived value for kerogen volume is below a threshold value (for porous kerogen volume, between 0.01 and 0.05, e.g. about 0.03), an input value for matrix slowness may be increased iteratively until the kerogen volume is above the threshold.

If the derived value for the matrix porosity is below a threshold value (0.03), then an input value for matrix slowness may be decreased iteratively until the matrix porosity is above the threshold.

The log data may include neutron log measurements. In this case, if the value for kerogen volume is below a threshold value (for porous kerogen volume, between 0.01 and 0.05, preferably about 0.03), an input value for solid matrix grain density is iteratively increased until the value for porous kerogen volume is above the threshold.

Alternatively, if the log data includes neutron log measurements and a value for porosity of mineral matrix is derived from said mathematical analysis, then, if the porosity of mineral matrix is below the same threshold value, the solid matrix neutron response may be increased iteratively until porosity of the mineral matrix is above the threshold. The above adjustments are summarized in Table 1 below

TABLE 1 Threshold parameter: Response: RHOB - DT model V_(pk) < 0.03 Increase DT_(sm) (sonic slowness) Phi_(pm) < 0.03 Decrease DT_(sm) (sonic slowness) RHOB - NPHI model V_(pk) < 0.03 Increase RHO_(sm) (solid matrix grain density) up to the limit of 2.75 Phi_(pm) < 0.03 Increase NP_(sm) (solid matrix neutron response) up to the limit of 0.20

In another optional, but very useful step, values derived from the mathematical analysis may be adjusted so that they are consistent with one or more log data inputs selected from bulk density, sonic slowness and neutron data.

In another embodiment, a method for evaluating the volume of hydrocarbon gas in a shale deposit comprises the steps of:

-   -   (i) Taking a log measurement of bulk density (RHOB);     -   (ii) Taking log measurements indicative of porosity and water         content (e.g. DT log, neutron log, resistivity log);     -   (iii) Mathematically combining an estimated value for density or         porosity of kerogen with the log measurements of steps (i)         and (ii) to derive directly an estimate for porous kerogen         volume and total porosity and water content.

The optional features explained above all apply to this embodiment.

In a further embodiment, a method for evaluating the volume of hydrocarbon gas in a shale deposit comprises the steps of:

-   -   drilling a well into the shale deposit and measuring log data         using a logging tool,         -   said log data comprising at least:         -   (i) bulk density data; and         -   (ii) either (a) neutron response data or (b) bulk slowness             data together with either resistivity data or neutron             response data;     -   from said log data, together with at least one known, estimated         or assumed parameter, directly computing an estimate of total         porosity, and porous kerogen volume, for the deposit;     -   wherein, said at least one known, estimated or assumed parameter         is selected from the group comprising solid mineral grain         density, slowness of the solid matrix, porosity of kerogen,         density of solid kerogen and slowness of solid kerogen.

The optional features explained above all apply to this further embodiment.

In a further embodiment, a difference value Del_(phi) is derived, essentially representing the difference between the neutron log (or slowness log) and porosity derived from the RHOB log. This value is found to be related to the clay volume Vclay_(ND), which in turn can be used to compute the solid matrix neutron response NP_(sm) (or slowness matrix response DT_(sm)). This NP_(sm) or DT_(sm) value is then used as an input parameter in the methods described above.

This further embodiment has the advantage that it eliminates one of the feedback loops which might otherwise used to put bounds around the results. This is explained in more detail later.

Definitions

Porosity is (Φ—sometimes referred to as “Phit”) is the volume fraction of pores in a matrix, either of mineral (Φ_(pm)) or of kerogen (Φ_(pk)).

Sonic slowness (DT) is a measure of the amount of time it takes a sound wave to travel a certain distance, the inverse of velocity. It is usually reported in micro-seconds/foot and symbolized as DT.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and benefits thereof may be acquired by referring to the following description taken in conjunction with the accompanying drawings in which:

FIG. 1 is a schematic illustration of the structure of a region of shale, with porosity;

FIGS. 2 a and 2 b are diagrams illustrating a difference in approach between prior art methods for evaluating shale (FIG. 2 a) and that of the invention (FIG. 2 b);

FIG. 3 is a diagram summarizing a model for the composition of a shale deposit; and

FIG. 4 is a plot of various log measurements and outputs from Example 1;

FIG. 5 is a plot of various log measurements and outputs from Example 2;

FIG. 6 is a plot of various log measurements and outputs from Example 3;

FIGS. 7 a and 7 b are plots of volume of pyrite vs. volume of kerogen for Examples 1 and 2 respectively;

FIG. 8 is a flow diagram showing the iterative solutions of the third and fourth embodiments, used to include effects of pyrite, water saturation, and changes in matrix properties;

FIG. 9 is a plot of Vpk vs. RHOB showing maximum and minimum matrix porosity bounds used in the methodology of the fifth embodiment; and

FIG. 10 is a plot of various log measurements and outputs from Example 4.

DETAILED DESCRIPTION

Turning now to the detailed description of the preferred arrangement or arrangements of the present invention, it should be understood that the inventive features and concepts may be manifested in other arrangements and that the scope of the invention is not limited to the embodiments described or illustrated. The scope of the invention is intended only to be limited by the scope of the claims that follow.

In a first embodiment of the invention, an evaluation method is based on bulk density and compressional sonic logs and, optionally, a resistivity log. These porosity logs were chosen since they each have a robust response to organic material. The advantage of this approach is the simplicity and minimal number of input logs that minimize data acquisition costs. Of course, the smaller number of inputs may require greater reliance on the use of assumptions.

This approach yields total porosity, volume of kerogen, volume of pyrite, whole rock grain density and water content, all five of which can be directly compared to core data if necessary to verify the results. Model input parameters (i.e. estimated parameters or parameters which are based on information from elsewhere) include log response properties of dry kerogen, kerogen porosity, properties of the saturating fluids, and properties of the non-kerogen mineral matrix. The model parameters are mostly assumed to be constant with depth, although they could be varied by petrofacies or zone.

The method is based on a new petrophysical model, shown in FIG. 3. This model explicitly includes solid kerogen and kerogen porosity in addition to other components normally observed in petrophysical models. The total porosity is the sum of the volumes of adsorbed gas, free gas (and liquid, if any) in kerogen pores, free gas and water (and liquid hydrocarbon, if any) in mineral matrix pores, and bound water associated with clay particles.

Water saturation for this model is computed as the sum of irreducible water plus free water divided by the total porosity, and is typically computed using Archie's Equation, though there are other saturation equations which could be used such as Simandoux, Dual Water or Waxman-Smits, all of which are well known to those skilled in this art. Archie's equation is given below:

${Sw} = \left\lbrack \frac{aRw}{{Phit}^{m}{Rt}} \right\rbrack^{1/n}$

Sw=water saturation, volume fraction of the pore space that is occupied by water

a=constant, usually a=1

Rw=resistivity of water contained in the pore space, ohm-m

Phit=porosity (volume fraction pore space)

m=cementation exponent, often m=2.0

Rt=formation resistivity, ohm-m

n=saturation exponent, often n=2.

In addition to using Archie's equation, which requires a resistivity log, it is also possible to compute water saturation using core data, for example core porosity and core bulk volume gas which often display a strong correlation. Intervals with low porosity are more likely to contain relatively smaller amounts of gas, and thus have higher values of water saturation.

${Sw} = \frac{\left( {{Phit} - {BVG}} \right)}{Phit}$

Referring to FIG. 1, in this embodiment, the formation is assumed to be composed of two components: porous mineral matrix and porous kerogen 1. The porous mineral matrix is composed of mineral grains 2 plus porosity 3, which can be gas-filled to its irreducible state, as presented in the model just described. The kerogen also contains porosity 4, which we assume to be hydrocarbon gas or liquid-filled based on the assumption that this material is oil wet; see Wang, F. P, and Reed, R. M., 2009, Pore Networks and Fluid Flow in Gas Shales: SPE 124253, presented at the 2009 SPE Annual conference, New Orleans, USA, 4-7 Oct., 2009.

Clay volume can be determined using an average of the two estimators based on resistivity and the neutron porosity, or using other methods. Although not specifically defined here as part of the petrophysical model, clay effects on the input logs can be accounted for in either of two ways. The first is to simply apply a correction to the input logs that is proportional to the clay volume. The second is to correct the matrix properties (DT_(sm), RHO_(sm), or NP_(sm)) by an amount proportional to the clay volume. The results in the Examples below were obtained using an adjustment to input parameter DT_(sm) related to the clay volume for the RHOB—DT model, and by subtracting an amount related to the clay volume from the input log NPHI for the RHOB—NPHI model.

Nomenclature

sm—solid matrix (dry, crystalline, includes clay)

pm—porous matrix

pmfl—fluid contained within porous matrix

sk—solid kerogen (dry)

pk—porous kerogen

pkfl—fluid contained within porous kerogen

DT—component slowness

RHO—component density

NP—component thermal neutron response

Input Logs

RHOB

Resistivity

DT or NPHI

Input Parameters

RHO_(sm)=solid mineral matrix grain density

RHO_(pmfl)=fluid density in solid matrix

RHO_(sk)=density of solid kerogen (nominally 1.3 g/c3)

RHO_(pkfl)=density of fluid contained in kerogen

RHO_(pk)=density of porous kerogen, including fluids OR Φ_(pk) kerogen porosity

DT_(sm)=slowness of the solid matrix

DT_(pmfl)=slowness of the fluid in porous matrix

DT_(sk)=slowness of solid kerogen

DT_(pkfl)=slowness of the fluid in porous kerogen

or

NP_(sm)=neutron response of the solid matrix

NP_(pmfl)=neutron response of the fluid in solid matrix

NP_(sk)=neutron response of solid kerogen

NP_(pkfl)=neutron response of the fluid in solid matrix

Basic Equations

RHOB=RHO_(pm) V _(pm)+RHO_(pk) V _(pk)

DT=DT _(pm) V _(pm)+DT_(pk) V _(pk)

1=V _(pm) +V _(pk)

RHO_(pm)=RHO_(sm)(1−Φ_(pm))+RHO_(pmfl)Φ_(pm)

or

RHOB=RHO_(pm) V _(pm)+RHO_(pk) V _(pk)

Nphi=NP_(pm) V _(pm)+NP_(pk) V _(pk)

1=V _(pm) +V _(pk)

RHO_(pm)=RHO_(sm)(1−Φ_(pm))+RHO_(pmfl)Φ_(pm)

Solution for the Model Based on RHOB and DT: Volume of Solid Kerogen:

V _(k) =V _(pk)(1−φ_(pk))

Where Φ_(pk) is the kerogen porosity, either given by (in the case in which RHO_(pk) is an input parameter)

$\varphi_{pk} = \frac{\left( {{rho}_{sk} - {rho}_{pk}} \right)}{\left( {{rho}_{sk} - {rho}_{pkfl}} \right)}$

or is an input parameter, in which case RHO_(pk) is given by:

rho_(pk)=rho_(sk)(1−φ_(pk))+rho_(pkfl)φ_(pk)

The density of solid kerogen RHO_(sk) (nominally 1.3 g/c3), together with either the porosity of the kerogen Φ_(pk), or the density of porous kerogen RHO_(pk), are input parameters.

The volume of porous kerogen V_(pk) is given by

$V_{pk} = \frac{{DT} - {DT}_{sm} - {\frac{c}{b}\left( {{rho}_{sm} - {rhob}} \right)}}{{DT}_{pk} - {DT}_{sm} - {\frac{c}{b}\left( {{rho}_{sm} - {rho}_{pk}} \right)}}$

where b=(RHO_(sm)−RHO_(pmfl)), and

c=(DT_(pmfl)−DT_(sm))

and DT_(pk)=DT_(sk) (1−Φ_(pk))+DT_(pkfl)Φ_(pk)

The porosity of the mineral matrix is

$\varphi_{pm} = \frac{\frac{\left( {{rhob} - {{rho}_{pk}V_{pk}}} \right)}{\left( {1 - V_{pk}} \right)} - {rho}_{sm}}{- b}$

Total porosity is the Phit=V _(pm)Φ_(pm) +V _(pk)Φ_(pk)

Finally, the grain density of the whole rock, including the kerogen is

${rhog} = \frac{{{rho}_{sm}\left( {1 - V_{sk} - {Phit}} \right)} + {{rho}_{sk}V_{sk}}}{\left( {1 - {Phit}} \right)}$

The high degree of correlation between different input parameters, the uncertainty in their value, and the iterative solution make it possible and desirable to have a means of checking that the final output results are consistent with the inputs logs. The approach is to compute the formation bulk density using the computed results:

RHOB=V _(k)Rho_(sk) +V _(pk)φ_(pk)Rho_(pkfl)+(V _(sm)+V_(py)+V_(Clay))Rho_(sm)+(V_(sm) +V _(py) +V _(Clay))φ_(pm)Rho_(pmfl)

A similar calculation is carried out for either DT or NPHI logs. If the computed RHOB, DT, or NPHI agrees with the input log, then the results are internally consistent. If there are differences between the computed and measured logs, then the various input parameters may be systematically adjusted until the logs agree, or the differences are minimized.

In a second embodiment of the invention, neutron logs are used for the porosity logs when the sonic log is not available. Neutron response is a technique for measuring porosity which is well known per se in this field. The second embodiment is otherwise identical to the first apart from substituting equivalent values for NP (e.g. NP_(sm), NP_(pmfl), etc.) in place of the values for DT. In the remaining examples, when DT/slowness values or parameters are discussed, it should be taken that these are interchangeable with NP values or parameters.

X-ray diffraction (XRD) data for all shale gas formations included in the examples below show measurable amounts of pyrite (and its dimorph marcasite, present in smaller amounts). Both minerals are composed of FeS₂, and have values of grain density of 5.02 and 4.88 g/cc. These values are significantly greater than the density values of the dominant host mineral matrix of quartz or calcite of 2.65 or 2.71 g/cc. When pyrite is present in small quantities, as in the case of shale gas formations, it can measurably increase the grain density of the matrix

Examination of core XRD data for a number of wells suggests that the volume of pyrite present is often correlated with the kerogen content. FIG. 7 shows data from two wells that establish this relationship.

In a third embodiment of the invention, the correlation of the pyrite volume with the kerogen volume is taken into account in the petrophysical model in addition to the procedure of the first or second embodiments. A direct linear relation of the volume of pyrite with the volume of kerogen is assumed; FIGS. 8 a and 8 b show this relationship based on data from Examples 1 and 2 below, respectively. The solid mineral matrix grain density in the model is re-computed to account for the pyrite content, as part of an interative solution used to correct for the effects of fluid saturation changes. A modification of the third embodiment might be to include the effects of pyrite on the matrix sonic slowness or the solid matrix neutron response. However, these adjustments would be small relative to other sources of variation in this parameter, and also do not have core data for verification.

The adjustment to correlate pyrite volume with kerogen volume is performed iteratively, as set out in the flow diagram of FIG. 8.

An additional complication may require further refinement of the solution. In some circumstances the computed volume of porous kerogen (V_(pk)) is a non-physical negative value, or near zero positive value. This is likely due to violated assumptions. A fourth embodiment of the invention involves adding a further procedure to the first, second or third embodiments in order to address this problem. The approach is iteratively to decrease the values of the matrix sonic slowness until V_(pk) is greater than an input threshold value (nominally a volume ratio of 0.03). Similarly, at times the computed matrix porosity has a negative value or near zero positive value. This can be handled by iteratively increasing the matrix slowness until the computed matrix porosity exceeds an input threshold value (nominally 0.03). Adjusting the slowness of the solid mineral matrix thus has the effect of maintaining lower bounds on both the volume of porous matrix and the mineral matrix porosity.

In a modification of the fourth embodiment, for the case where NPHI is used in place of DT, different adjustments are required to maintain lower bounds on the volume of porous kerogen and the porosity of the mineral matrix. If the volume of porous kerogen is less than some input threshold value (nominally 0.03), then the grain density of the solid matrix is systematically increased up to a limiting value (nominally 2.75 g/cc). If, on the other hand, the porosity of the mineral matrix is less than some threshold value (nominally 0.03), then the NPHI response parameter for the mineral matrix (NP_(sm)) is systematically increased up to a limiting value (nominally 0.20).

The results of these adjustments are generally consistent with core data, total porosity values that are rarely less than 0.03, and porous kerogen values that are rarely less than 0.03. These two iterative processes are shown together with the pyrite iteration of the third embodiment in FIG. 8.

A fifth embodiment makes use of the difference value Del_(phi), as discussed above. Del_(phi) essentially represents the difference between the neutron log (or slowness log) and porosity derived from the RHOB log.

We start with the basic, well known equation for RHOB:

RHOB=RHO_(m) V _(m)+RHO_(fl)Phit,

where

-   RHO_(m)=density of the rock matrix; -   RHO_(fl)=density of the fluid in the pore space; -   V_(sm)=1−Phit; -   Phit=volume of the total pore space. -   The equation for RHOB can be solved for Phit:

Phit=(RHO_(m)−RHOB)/(RHO_(m)−RHO_(fl)).

By taking a nominal assumed value for matrix density RHO_(m) (in the equations below it has been taken as 2.71 g/cm³ which is appropriate for limestone, but it could be a different value such as 2.65 g/cm³ which would be appropriate for quartz) and a nominal value for water density RHO_(fl) (in the equations below it is 1.04 g/cm³—this will vary with assumed level of salinity), then the following equation can be derived:

Del_(phi)=Nphi−(2.71−RHOB)/(2.71−1.04)

This equation, and the remainder of the discussion below, assumes that the neutron log is being used, but the slowness log may be substituted and the analysis remains equally valid provided the slowness log is converted to a sonic porosity using the equation:

PhiDT=(DT_(ma)−DT)/(DT_(ma)−DT_(fl))

Del_(phi) essentially represents the separation between the two logs when they are plotted in the same track in a log plot. The total porosity values as measured by each tool cancel or offset each other in Del_(phi). The values of apparent porosity due to solid kerogen also tend to cancel out as well. Thus Del_(phi) primarily reflects the influence of various minerals on the two logs. The difference has a slight influence from the dominant matrix mineral (often quartz or calcite) and a stronger influence from the clay content. Therefore the difference term can be used to estimate the volume of clay Vclay_(ND:)

Vclay_(ND) =X*(Del_(phi) +Y), where X and Y are scale and offset parameters.

Next, Vclay_(ND) is used to compute the neutron response (NP_(sm)) and grain density (RHO_(sm)) of the solid mineral matrix using values of scale and offset parameters that lead to a match between the computed results and the measured logs.

The values of these two quantities are used in the general solution given by the equations in section [0055] above, along with the iterative solution used to obtain values for the pyrite content (dependent on kerogen content), and to account for the effects of changes in fluid saturation. This embodiment thus employs the outer iterative loop shown in FIG. 9, but replaces the inner iterative loops that produce change in the matrix properties.

The solution as just described sometimes results in kerogen content that exceeds physical bounds, determined by considering the existence of maximum and minimum values of the matrix porosity. Consider a cross plot of volume of porous kerogen (Vpk) on the vertical axis, and the formation bulk density (RHOB) on the horizontal axis as shown in FIG. 8. An upper bound on Vpk is formed by the straight line that connects the two points A and B as shown on FIG. 9.

Point A: Porous kerogen endpoint:

Vpk=1.0, RHOB=rho_(sk)(1−Φ_(pk))+rho_(fl)Φ_(pk)

Point B: Porous matrix endpoint (minimum porosity case):

Vpk=0, RHOB=rho_(pm)(1−Φ_(pm,Min))+rho_(fl)Φ_(pm,Min)

Similarly, the lower bound on Vpk is formed by the straight line that connects the two points A and C, with point C given as follows.

Point C: Porous matrix endpoint (maximum porosity case):

Vpk=0, RHOB=rho_(pm)(1−Φ_(pm,Max))+rho_(fl)Φ_(pm,Max)

The Vpk is also constrained to be non-negative.

These constraints are shown in FIG. 9 which is a plot of Vpk vs. RHOB.

In the foregoing embodiments, various model input parameters are used which are known or estimated from previous experiences with shale deposits. Table 2 shows some values for these parameters.

TABLE 2 Log Model Component Properties Density DT NPHI* Component (g/cc) μsec/ft V/V Reference Quartz 2.64 56.0 −0.02 Schlumberger** Calcite 2.71 49.0 0.0 Schlumberger** Dolomite 2.85 44.0 0.01 Schlumberger** Pyrite 4.99 39.2 −0.03 Schlumberger** Lignite 1.19 160 0.52 Bitumin 1.24 120 0.60+ Kerogen 1.0-1.1 160 50-65 2 H.I. Water at 200° F. 0.98 208 1.0 Water + 10% 1.05 192.3 0.96 NaCl at 200° F. Water + 20% 1.125 181.8 0.92 NaCl at 200° F. Kerosene 60° F. 0.82 230 1.04 Methane 0.24 0.48 7,000 psi, 200° F.** Air at 3,000 psi, 780 212° F. 1 density of water at 200° F. and 7,000 psi *Limestone units **Schlumberger Log Interpretation Charts 2005 Edition. Published by Schlumberger Marketing Communications 2 New Evaluation Techniques for Gas Shale Reservoirs, Lewis, R., et al, Schlumberger Reservoir Symposium 2004

EXAMPLE 1

FIG. 4 shows a plot for a ConocoPhillips field location which will be referred to as field location A. This plot shows measured and computed logs which are detailed further in Table 2 below. The solid computed lines were obtained using a RHOB-DT model according to the first, third and fourth embodiments; the dashed lines were obtained using a RHOB-NPHI model according to the second, third and fourth embodiments. Where there is more than one label, the upper label for each track is the name of data from core samples, which are shown as open circles on the respective track. The middle and lower labels represent data from the RHOB-DT and RHOB-NPHI models, respectively. For each track the scale is indicated at the top; this scale is reproduced in Table 3 since it can be hard to read in the Figure.

FIG. 4 shows the excellent correlation between the derived results with core data, shown as open circles on the plot. FIGS. 5 and 6, relating to Examples 2 and 3 below, also show good correlation with core data.

TABLE 3 Track labels Parameter (unit) Data range GR Gamma (GAPI)  0 to 200  RD Deep resistivity 0.2 to 2000 (Ohm-meters) DTC Compressional slowness 140 to 40   (microseconds/ft) RHOB Bulk density (g/cc) 1.95 to 2.95   VCLAY Clay volume 0 to 1   (volume ratio) PYRITE/VPY (volume ratio) 0 to 0.1 KEROGEN/VSK (volume ratio) 0 to 0.2 PHI/PHIT (volume ratio) 0 to 0.2 RHOMA/RHOG (volume ratio) 2.6 to 2.8   SW/SWT (volume ratio) 0 to 1   BVH/BVG (volume ratio) 0 to 0.1

EXAMPLE 2

FIG. 5 shows a plot for a ConocoPhillips field location which will be referred to as field location B. This plot shows measured and computed logs whose details are given in Table 3 above The solid computed lines were obtained using a RHOB-DT model according to the first, third and fourth embodiments; the dashed lines were obtained using a RHOB-NPHI model according to the second, third and fourth embodiments.

EXAMPLE 3

FIG. 5 shows a plot for a ConocoPhillips field location which will be referred to as field location C. This plot shows measured and computed logs whose details are given in Table 3 above. The solid computed lines were obtained using a RHOB-DT model according to the first, third and fourth embodiments; the dashed lines were obtained using a RHOB-NPHI model according to the second, third and fourth embodiments.

EXAMPLE b 4

FIG. 10 shows a plot for a ConocoPhillips field location which will be referred to as field location D. This plot shows measured and computed logs similar to those of the previous examples. The computed lines were obtained using a methodology according to the fifth embodiment.

In closing, it should be noted that the discussion of any reference is not an admission that it is prior art to the present invention, especially any reference that may have a publication date after the priority date of this application. At the same time, each and every claim below is hereby incorporated into this detailed description or specification as a additional embodiments of the present invention.

Although the systems and processes described herein have been described in detail, it should be understood that various changes, substitutions, and alterations can be made without departing from the spirit and scope of the invention as defined by the following claims. Those skilled in the art may be able to study the preferred embodiments and identify other ways to practice the invention that are not exactly as described herein. It is the intent of the inventors that variations and equivalents of the invention are within the scope of the claims while the description, abstract and drawings are not to be used to limit the scope of the invention. The invention is specifically intended to be as broad as the claims below and their equivalents.

REFERENCES

All of the references cited herein are expressly incorporated by reference. The discussion of any reference is not an admission that it is prior art to the present invention, especially any reference that may have a publication data after the priority date of this application. Incorporated references are listed again here for convenience:

-   1. Passey, Q., et al., A practical model for organic richness from     porosity and resistivity logs, AAPG Bulletin, 74, No. 12, p.     1777-1794. -   2. Lewis, R., et al, New Evaluation Techniques for Gas Shale     Reservoirs, Schlumberger Reservoir Symposium 2004 -   3. Wang, F. P, and Reed, R. M., 2009, Pore Networks and Fluid Flow     in Gas Shales: SPE 124253, presented at the 2009 SPE Annual     conference, New Orleans, USA, 4-7 Oct., 2009. -   4. Schmoker, James W. and Hester, Timothy C., 1983, Organic carbon     in Bakken Formation, United States portion of Williston Basin: AAPG     Bulletin, v. 67, no. 12, p. 2165-2174 

1. A method for evaluating the volume of hydrocarbon gas or liquid in a shale deposit, the method comprising combining known kerogen density and/or kerogen porosity values with log data in a mathematical analysis to derive directly values for kerogen volume, total porosity and water content.
 2. The method according to claim 1 wherein said mathematical analysis comprises the solution of simultaneous equations incorporating said known kerogen density and/or kerogen porosity values and log data.
 3. The method according to claim 1 wherein said log data includes: (i) Log measurements of bulk density (RHOB) (ii) Log measurements indicative of porosity.
 4. The method according to claim 3, wherein said log measurements indicative of porosity comprise either or both of neutron log measurements and slowness measurements.
 5. The method according to claim 2, wherein said log data includes resistivity, providing an indication of water saturation.
 6. The method according to claim 1, wherein said mathematical analysis incorporates at least one known, assumed or estimated parameter from the group comprising: solid matrix grain density, matrix slowness, neutron response of solid matrix and slowness of solid kerogen.
 7. The method according to claim 6 wherein all said known, assumed or estimated parameters are incorporated in said mathematical analysis.
 8. The method according to claim 1 further comprising adjusting said derived value for kerogen volume to be consistent with a value for pyrite and/or marcasite volume based on an empirically derived relationship between kerogen volume and pyrite and/or marcasite volume.
 9. The method according to claim 8 comprising an iterative process.
 10. The method according to claim 8 wherein said value for pyrite and/or marcasite volume is obtained from an X ray diffraction analysis.
 11. The method according to claim 6 wherein, if said derived value for kerogen volume is below a threshold value, then either: (a) an input value for matrix slowness is iteratively increased until said derived value for kerogen volume is above said threshold value; or (b) if said log data includes neutron log measurements then, if said derived value for kerogen volume is below said threshold value, an input value for solid matrix grain density is iteratively increased until said derived value for porous matrix volume is above said threshold value.
 12. The method according to claim 11 wherein, in terms of porous kerogen, said threshold value is between 0.01 and 0.05, preferably about 0.03.
 13. The method according to claim 6 wherein a value for porosity of mineral matrix is derived from said mathematical analysis and wherein, if said derived value for porosity of mineral matrix is below a threshold value, then either: (a) an input value for matrix slowness is iteratively decreased until said derived value for porosity of mineral matrix is above said threshold value; or (b) if said log data includes neutron log measurements then, if said derived value for porosity of mineral matrix is below said threshold value, then solid matrix neutron response is iteratively increased until said derived value for porosity of mineral matrix is above said threshold value.
 14. The method according to claim 13 wherein said threshold value is between 0.01 and 0.05, preferably about 0.03.
 15. The method according to claim 1 further comprising adjusting values derived from mathematical analysis so that they are consistent with one or more log data inputs selected from bulk density, sonic slowness and neutron data.
 16. The method according to claim 6 wherein either (i) a value for solid matrix density RHO_(sm) and solid matrix neutron response NP_(sm) is derived from the difference between porosity derived from the RHOB log and the neutron log or alternatively (ii) a value for solid matrix density RHO_(sm) and solid matrix slowness DT_(sm) is derived from the difference between the RHOB porosity log and porosity derived from the slowness log, and said derived value for solid matrix neutron response NP_(sm) or said derived value for solid matrix slowness DT_(sm) is incorporated in said mathematical analysis.
 17. The method according to claim 16 wherein a value Vclay_(ND) for clay volume is computed from said difference, RHO_(sm) and either NP_(sm) or DT_(sm) then being calculated from the Vclay_(ND) value. 